Antarctica - ASV analysis

Get the Antarctica ASVs

set id description
16 Antar_2015_18S_V4
17 Antar_2015_16S_plastid
18 Antar_2015_18S_V4_sorted
  • Only use asv for which supergroup_boot >= 90
Phyloseq - 18S filter
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 2310 taxa and 123 samples ]
sample_data() Sample Data:       [ 123 samples by 61 sample variables ]
tax_table()   Taxonomy Table:    [ 2310 taxa by 8 taxonomic ranks ]
============================
Phyloseq - 16S plastid
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 442 taxa and 103 samples ]
sample_data() Sample Data:       [ 103 samples by 61 sample variables ]
tax_table()   Taxonomy Table:    [ 442 taxa by 8 taxonomic ranks ]
============================
Phyloseq - 18S sort
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 385 taxa and 60 samples ]
sample_data() Sample Data:       [ 60 samples by 61 sample variables ]
tax_table()   Taxonomy Table:    [ 385 taxa by 8 taxonomic ranks ]
============================
 [1] "sample_id"                      "file_name"                     
 [3] "sample_name"                    "sample_code"                   
 [5] "metadata_code"                  "replicate"                     
 [7] "DNA_RNA"                        "fraction_name"                 
 [9] "fraction_min"                   "fraction_max"                  
[11] "sample_concentration"           "sample_sorted"                 
[13] "reads_total"                    "sample_remark"                 
[15] "metadata_id"                    "metadata_code_original"        
[17] "project"                        "cruise"                        
[19] "station_id"                     "station_id_num"                
[21] "year"                           "date"                          
[23] "time"                           "season"                        
[25] "depth_level"                    "depth"                         
[27] "substrate"                      "substrate_description"         
[29] "substrate_description_detailed" "experiment_name"               
[31] "experiment_time"                "experiment_time_unit"          
[33] "experiment_bottle"              "experiment_condition"          
[35] "latitude"                       "longitude"                     
[37] "site_name"                      "country"                       
[39] "oceanic_region"                 "bottom_depth"                  
[41] "temperature"                    "salinity"                      
[43] "pH"                             "O2"                            
[45] "fluorescence"                   "ice_coverage"                  
[47] "Chla"                           "NO2"                           
[49] "NO3"                            "PO4"                           
[51] "Si"                             "Chla_0.2_3 um"                 
[53] "bact_ml"                        "syn_ml"                        
[55] "peuk_ml"                        "neuk_ml"                       
[57] "crypto_ml"                      "virus_small_ml"                
[59] "virus_large_ml"                 "metadata_remark"               
[61] "sample_label"                  

Filtration

  • Only use Station 6 (not 14)
  • Only surface considered (5 m)
  • Do not consider TFF samples
  • Only keep photosynthetic groups abd exclude dinoflagellates
  • Very important, must remove taxa that are not present in the filtered samples
Phyloseq - 18S filter
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 394 taxa and 50 samples ]
sample_data() Sample Data:       [ 50 samples by 61 sample variables ]
tax_table()   Taxonomy Table:    [ 394 taxa by 8 taxonomic ranks ]
============================
Phyloseq - 16S plastid
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 236 taxa and 40 samples ]
sample_data() Sample Data:       [ 40 samples by 61 sample variables ]
tax_table()   Taxonomy Table:    [ 236 taxa by 8 taxonomic ranks ]
============================
Phyloseq - 18S sort
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 114 taxa and 16 samples ]
sample_data() Sample Data:       [ 16 samples by 61 sample variables ]
tax_table()   Taxonomy Table:    [ 114 taxa by 8 taxonomic ranks ]
============================

Get the different fraction separately

Phyloseq - 18S filter 0.2 um
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 196 taxa and 17 samples ]
sample_data() Sample Data:       [ 17 samples by 61 sample variables ]
tax_table()   Taxonomy Table:    [ 196 taxa by 8 taxonomic ranks ]
============================
Phyloseq - 18S filter 3 um
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 229 taxa and 18 samples ]
sample_data() Sample Data:       [ 18 samples by 61 sample variables ]
tax_table()   Taxonomy Table:    [ 229 taxa by 8 taxonomic ranks ]
============================
Phyloseq - 18S filter 20 um
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 201 taxa and 15 samples ]
sample_data() Sample Data:       [ 15 samples by 61 sample variables ]
tax_table()   Taxonomy Table:    [ 201 taxa by 8 taxonomic ranks ]
============================
Phyloseq - 16S plastid 0.2 um
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 112 taxa and 11 samples ]
sample_data() Sample Data:       [ 11 samples by 61 sample variables ]
tax_table()   Taxonomy Table:    [ 112 taxa by 8 taxonomic ranks ]
============================
Phyloseq - 16S plastid 3 um
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 142 taxa and 16 samples ]
sample_data() Sample Data:       [ 16 samples by 61 sample variables ]
tax_table()   Taxonomy Table:    [ 142 taxa by 8 taxonomic ranks ]
============================
Phyloseq - 16S plastid 20 um
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 120 taxa and 13 samples ]
sample_data() Sample Data:       [ 13 samples by 61 sample variables ]
tax_table()   Taxonomy Table:    [ 120 taxa by 8 taxonomic ranks ]
============================
Phyloseq - 18S sort pico
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 65 taxa and 8 samples ]
sample_data() Sample Data:       [ 8 samples by 61 sample variables ]
tax_table()   Taxonomy Table:    [ 65 taxa by 8 taxonomic ranks ]
============================
Phyloseq - 18S sort nano
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 70 taxa and 8 samples ]
sample_data() Sample Data:       [ 8 samples by 61 sample variables ]
tax_table()   Taxonomy Table:    [ 70 taxa by 8 taxonomic ranks ]
============================

Normalize and transform to long form

18S filter
============================
========== 
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 394 taxa and 50 samples ]
sample_data() Sample Data:       [ 50 samples by 61 sample variables ]
tax_table()   Taxonomy Table:    [ 394 taxa by 8 taxonomic ranks ]

==========
The median number of reads used for normalization is  23202
16S plastid
============================
========== 
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 236 taxa and 40 samples ]
sample_data() Sample Data:       [ 40 samples by 61 sample variables ]
tax_table()   Taxonomy Table:    [ 236 taxa by 8 taxonomic ranks ]

==========
The median number of reads used for normalization is  28885
18S sort
============================
========== 
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 114 taxa and 16 samples ]
sample_data() Sample Data:       [ 16 samples by 61 sample variables ]
tax_table()   Taxonomy Table:    [ 114 taxa by 8 taxonomic ranks ]

==========
The median number of reads used for normalization is  27275
18S filter 0.2 um
============================
========== 
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 196 taxa and 17 samples ]
sample_data() Sample Data:       [ 17 samples by 61 sample variables ]
tax_table()   Taxonomy Table:    [ 196 taxa by 8 taxonomic ranks ]

==========
The median number of reads used for normalization is  14647
18S filter 3 um
============================
========== 
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 229 taxa and 18 samples ]
sample_data() Sample Data:       [ 18 samples by 61 sample variables ]
tax_table()   Taxonomy Table:    [ 229 taxa by 8 taxonomic ranks ]

==========
The median number of reads used for normalization is  24783
18S filter 20 um
============================
========== 
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 201 taxa and 15 samples ]
sample_data() Sample Data:       [ 15 samples by 61 sample variables ]
tax_table()   Taxonomy Table:    [ 201 taxa by 8 taxonomic ranks ]

==========
The median number of reads used for normalization is  29427
16S plastid 0.2 um
============================
========== 
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 112 taxa and 11 samples ]
sample_data() Sample Data:       [ 11 samples by 61 sample variables ]
tax_table()   Taxonomy Table:    [ 112 taxa by 8 taxonomic ranks ]

==========
The median number of reads used for normalization is  28471
16S plastid 3 um
============================
========== 
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 142 taxa and 16 samples ]
sample_data() Sample Data:       [ 16 samples by 61 sample variables ]
tax_table()   Taxonomy Table:    [ 142 taxa by 8 taxonomic ranks ]

==========
The median number of reads used for normalization is  31801
16S plastid 20 um
============================
========== 
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 120 taxa and 13 samples ]
sample_data() Sample Data:       [ 13 samples by 61 sample variables ]
tax_table()   Taxonomy Table:    [ 120 taxa by 8 taxonomic ranks ]

==========
The median number of reads used for normalization is  25641
18S sort pico
============================
========== 
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 65 taxa and 8 samples ]
sample_data() Sample Data:       [ 8 samples by 61 sample variables ]
tax_table()   Taxonomy Table:    [ 65 taxa by 8 taxonomic ranks ]

==========
The median number of reads used for normalization is  33258
18S sort nano
============================
========== 
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 70 taxa and 8 samples ]
sample_data() Sample Data:       [ 8 samples by 61 sample variables ]
tax_table()   Taxonomy Table:    [ 70 taxa by 8 taxonomic ranks ]

==========
The median number of reads used for normalization is  21650

Heatmaps (using the 10% most abundant taxa)

Class


========== 
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 7 taxa and 17 samples ]
sample_data() Sample Data:       [ 17 samples by 61 sample variables ]
tax_table()   Taxonomy Table:    [ 7 taxa by 8 taxonomic ranks ]

==========
The median number of reads used for normalization is  13808


========== 
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 7 taxa and 18 samples ]
sample_data() Sample Data:       [ 18 samples by 61 sample variables ]
tax_table()   Taxonomy Table:    [ 7 taxa by 8 taxonomic ranks ]

==========
The median number of reads used for normalization is  23366


========== 
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 5 taxa and 15 samples ]
sample_data() Sample Data:       [ 15 samples by 61 sample variables ]
tax_table()   Taxonomy Table:    [ 5 taxa by 8 taxonomic ranks ]

==========
The median number of reads used for normalization is  27860


========== 
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 4 taxa and 11 samples ]
sample_data() Sample Data:       [ 11 samples by 61 sample variables ]
tax_table()   Taxonomy Table:    [ 4 taxa by 8 taxonomic ranks ]

==========
The median number of reads used for normalization is  27592


========== 
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 5 taxa and 16 samples ]
sample_data() Sample Data:       [ 16 samples by 61 sample variables ]
tax_table()   Taxonomy Table:    [ 5 taxa by 8 taxonomic ranks ]

==========
The median number of reads used for normalization is  31460


========== 
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 4 taxa and 13 samples ]
sample_data() Sample Data:       [ 13 samples by 61 sample variables ]
tax_table()   Taxonomy Table:    [ 4 taxa by 8 taxonomic ranks ]

==========
The median number of reads used for normalization is  24881


========== 
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 4 taxa and 8 samples ]
sample_data() Sample Data:       [ 8 samples by 61 sample variables ]
tax_table()   Taxonomy Table:    [ 4 taxa by 8 taxonomic ranks ]

==========
The median number of reads used for normalization is  32016


========== 
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 4 taxa and 8 samples ]
sample_data() Sample Data:       [ 8 samples by 61 sample variables ]
tax_table()   Taxonomy Table:    [ 4 taxa by 8 taxonomic ranks ]

==========
The median number of reads used for normalization is  21054

Genus


========== 
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 11 taxa and 17 samples ]
sample_data() Sample Data:       [ 17 samples by 61 sample variables ]
tax_table()   Taxonomy Table:    [ 11 taxa by 8 taxonomic ranks ]

==========
The median number of reads used for normalization is  12475


========== 
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 6 taxa and 18 samples ]
sample_data() Sample Data:       [ 18 samples by 61 sample variables ]
tax_table()   Taxonomy Table:    [ 6 taxa by 8 taxonomic ranks ]

==========
The median number of reads used for normalization is  20234


========== 
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 9 taxa and 15 samples ]
sample_data() Sample Data:       [ 15 samples by 61 sample variables ]
tax_table()   Taxonomy Table:    [ 9 taxa by 8 taxonomic ranks ]

==========
The median number of reads used for normalization is  26305


========== 
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 4 taxa and 11 samples ]
sample_data() Sample Data:       [ 11 samples by 61 sample variables ]
tax_table()   Taxonomy Table:    [ 4 taxa by 8 taxonomic ranks ]

==========
The median number of reads used for normalization is  27286


========== 
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 7 taxa and 16 samples ]
sample_data() Sample Data:       [ 16 samples by 61 sample variables ]
tax_table()   Taxonomy Table:    [ 7 taxa by 8 taxonomic ranks ]

==========
The median number of reads used for normalization is  30698


========== 
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 5 taxa and 13 samples ]
sample_data() Sample Data:       [ 13 samples by 61 sample variables ]
tax_table()   Taxonomy Table:    [ 5 taxa by 8 taxonomic ranks ]

==========
The median number of reads used for normalization is  24093


========== 
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 6 taxa and 8 samples ]
sample_data() Sample Data:       [ 8 samples by 61 sample variables ]
tax_table()   Taxonomy Table:    [ 6 taxa by 8 taxonomic ranks ]

==========
The median number of reads used for normalization is  29874


========== 
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 5 taxa and 8 samples ]
sample_data() Sample Data:       [ 8 samples by 61 sample variables ]
tax_table()   Taxonomy Table:    [ 5 taxa by 8 taxonomic ranks ]

==========
The median number of reads used for normalization is  20315

2019-10-04